code: def test_observations_are_invariant_to_the_player_trained_wt_step():
      p_red_pos = [
          [0, 0],
          [0, 0],
          [1, 1],
          [1, 1],
          [0, 0],
          [1, 1],
          [2, 0],
          [0, 1],
          [2, 2],
          [1, 2],
      ]
      p_blue_pos = [
          [0, 0],
          [0, 0],
          [1, 1],
          [1, 1],
          [1, 1],
          [0, 0],
          [0, 1],
          [2, 0],
          [1, 2],
          [2, 2],
      ]
      p_red_act = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
      p_blue_act = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
      c_red_pos = [
          [1, 1],
          None,
          [0, 1],
          None,
          None,
          [2, 2],
          [0, 0],
          None,
          None,
          [2, 1],
      ]
      c_blue_pos = [
          None,
          [1, 1],
          None,
          [0, 1],
          [2, 2],
          None,
          None,
          [0, 0],
          [2, 1],
          None,
      ]
      max_steps, batch_size, grid_size = 10, 52, 3
      n_steps = max_steps
      envs = init_several_env(max_steps, batch_size, grid_size)
  
      batch_deltas = [
          i % max_steps if i % 2 == 0 else i % max_steps - 1
          for i in range(batch_size)
      ]
  
      for env_i, env in enumerate(envs):
          _, _ = env.reset()
          step_i = 0
  
          for _ in range(n_steps):
              overwrite_pos(
                  step_i,
                  batch_deltas,
                  max_steps,
                  env,
                  p_red_pos,
                  p_blue_pos,
                  c_red_pos,
                  c_blue_pos,
              )
              actions = {
                  "player_red": [
                      p_red_act[(step_i + delta) % max_steps]
                      for delta in batch_deltas
                  ],
                  "player_blue": [
                      p_blue_act[(step_i + delta) % max_steps]
                      for delta in batch_deltas
                  ],
              }
              obs, reward, done, truncated, info = env.step(actions)
  
              step_i += 1
              # assert observations are symmetrical respective to the actions
              if step_i % 2 == 1:
                  obs_step_odd = obs
              elif step_i % 2 == 0:
                  assert np.all(
                      obs[env.players_ids[0]]
                      == obs_step_odd[env.players_ids[1]]
                  )
                  assert np.all(
                      obs[env.players_ids[1]]
                      == obs_step_odd[env.players_ids[0]]
                  )
  
              if step_i == max_steps:
                  break
  
imports: import copy
  import random
  import numpy
  from ray.rllib.examples.env.coin_game_vectorized_env import VectorizedCoinGame, AsymVectorizedCoinGame
  from ray.rllib.examples.env.tests.test_coin_game_non_vectorized_env import assert_obs_is_symmetrical
label_desc: Use image node_id 24 for calling a global function with example usage: test_observations_are_invariant_to_the_player_trained_wt_step() without return types